Arvind Mukundan^1,2, Riya Karmakar¹, Devansh Gupta³, Hsiang-Chen Wang^1,4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069646 - 10 November 2025

Abstract Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0. Manual inspection of products on assembly lines remains inefficient, prone to errors and lacks consistency, emphasizing the need for a reliable and automated inspection system. Leveraging both object detection and image segmentation approaches, this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning (DL) models. Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images… More >

Asrar Mahboob¹, Muhammad Rashad¹, Ghulam Abbas¹, Zohaib Mushtaq², Tehseen Mazhar^3,*, Ateeq Ur Rehman^4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3805-3823, 2025, DOI:10.32604/cmc.2025.067615 - 23 September 2025

Abstract This paper presents a novel blockchain-embedded cybersecurity framework for industrial solar power systems, integrating immutable machine learning (ML) with distributed ledger technology. Our contribution focused on three factors, Quantum-resistant feature engineering using the UNSW-NB15 dataset adapted for solar infrastructure anomalies. An enhanced Light Gradient Boosting Machine (LightGBM) classifier with blockchain-validated decision thresholds, and A cryptographic proof-of-threat (PoT) consensus mechanism for cyber attack verification. The proposed Immutable LightGBM model with majority voting and cryptographic feature encoding achieves 96.9% detection accuracy with 0.97 weighted average of precision, recall and F1-score, outperforming conventional intrusion detection systems (IDSs) by… More >

Gumashvili Megi^1,2, Yiping Mu¹, Kulbo Nora Bakabbey³, Addo Prince Clement^2,3,4,*, Kiti Kanokon¹, Menezes Dalila Batista de Sousa de¹, Baidoo Bernard Ekow¹

Journal of Psychology in Africa, Vol.35, No.3, pp. 287-298, 2025, DOI:10.32604/jpa.2025.067165 - 31 July 2025

Abstract This study investigated the influence of human capital development and technological, strategic, cognitive, and environmental factors on Industry 4.0 readiness, as well as cultural factors acting as a mediator. Respondents were 478 employees from across eight regions in Russia. Survey data were collected on employee technological readiness, human capital development, strategic planning, cognitive perceptions, and environmental and cultural factors influencing the adoption of Industry 4.0 technologies, with cultural factors mediating. These findings from the structure equation analysis show that technological factors and human capital development are the strongest predictors of readiness, suggesting that robust digital More >

Mudasir Dilawar^*, Muhammad Shahbaz

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5091-5114, 2025, DOI:10.32604/cmc.2025.064090 - 19 May 2025

Abstract In the era of Industry 4.0, condition monitoring has emerged as an effective solution for process industries to optimize their operational efficiency. Condition monitoring helps minimize unplanned downtime, extending equipment lifespan, reducing maintenance costs, and improving production quality and safety. This research focuses on utilizing Bayesian search-based machine learning and deep learning approaches for the condition monitoring of industrial equipment. The study aims to enhance predictive maintenance for industrial equipment by forecasting vibration values based on domain-specific feature engineering. Early prediction of vibration enables proactive interventions to minimize downtime and extend the lifespan of critical… More >

Borja Bordel Sánchez^1,*, Fernando Rodríguez-Sela¹, Ramón Alcarria², Tomás Robles¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1821-1853, 2025, DOI:10.32604/cmc.2025.059012 - 16 April 2025

Abstract The Industry 4.0 revolution is characterized by distributed infrastructures where data must be continuously communicated between hardware nodes and cloud servers. Specific lightweight cryptosystems are needed to protect those links, as the hardware node tends to be resource-constrained. Then Pseudo Random Number Generators are employed to produce random keys, whose final behavior depends on the initial seed. To guarantee good mathematical behavior, most key generators need an unpredictable voltage signal as input. However, physical signals evolve slowly and have a significant autocorrelation, so they do not have enough entropy to support high-randomness seeds. Then, electronic… More >

Vidyalakshmi G¹, S Gopikrishnan^2,*, Wadii Boulila³, Anis Koubaa³, Gautam Srivastava^4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 51-113, 2025, DOI:10.32604/cmes.2025.057788 - 11 April 2025

Abstract Cyber-Physical Systems (CPS) represent an integration of computational and physical elements, revolutionizing industries by enabling real-time monitoring, control, and optimization. A complementary technology, Digital Twin (DT), acts as a virtual replica of physical assets or processes, facilitating better decision making through simulations and predictive analytics. CPS and DT underpin the evolution of Industry 4.0 by bridging the physical and digital domains. This survey explores their synergy, highlighting how DT enriches CPS with dynamic modeling, real-time data integration, and advanced simulation capabilities. The layered architecture of DTs within CPS is examined, showcasing the enabling technologies and… More >

Mehwash Farooqui¹, Atta Rahman^2,*, Latifa Alsuliman¹, Zainab Alsaif¹, Fatimah Albaik¹, Cadi Alshammari¹, Razan Sharaf¹, Sunday Olatunji¹, Sara Waslallah Althubaiti¹, Hina Gull³

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2587-2605, 2024, DOI:10.32604/cmc.2024.055262 - 18 November 2024

Abstract The proposed study focuses on the critical issue of corrosion, which leads to significant economic losses and safety risks worldwide. A key area of emphasis is the accuracy of corrosion detection methods. While recent studies have made progress, a common challenge is the low accuracy of existing detection models. These models often struggle to reliably identify corrosion tendencies, which are crucial for minimizing industrial risks and optimizing resource use. The proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network (CNN), as well as two pretrained… More >

Sohaib Latif^1,*, M. Saad Bin Ilyas¹, Azhar Imran², Hamad Ali Abosaq³, Abdulaziz Alzubaidi⁴, Vincent Karovič Jr.⁵

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 353-379, 2024, DOI:10.32604/iasc.2024.047080 - 21 May 2024

Abstract The Internet of Things (IoT) is growing rapidly and impacting almost every aspect of our lives, from wearables and healthcare to security, traffic management, and fleet management systems. This has generated massive volumes of data and security, and data privacy risks are increasing with the advancement of technology and network connections. Traditional access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF failure. Additionally, conventional privacy preservation methods have high latency costs and overhead for resource-constrained devices. Previous machine learning approaches were… More >

Cheng Wang^1,2,*, Zhuowei Zheng¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1563-1592, 2024, DOI:10.32604/cmes.2023.031103 - 17 November 2023

Abstract The advent of Industry 4.0 has compelled businesses to adopt digital approaches that combine software to enhance production efficiency. In this rapidly evolving market, software development is an ongoing process that must be tailored to meet the dynamic needs of enterprises. However, internal research and development can be prohibitively expensive, driving many enterprises to outsource software development and upgrades to external service providers. This paper presents a software upgrade outsourcing model for enterprises and service providers that accounts for the impact of market fluctuations on software adaptability. To mitigate the risk of adverse selection due… More >

Mohamed Zarouan¹, Ibrahim M. Mehedi^1,2,*, Shaikh Abdul Latif³, Md. Masud Rana⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1341-1364, 2024, DOI:10.32604/cmes.2023.030037 - 17 November 2023

Abstract Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamless operation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0. Specifically, various modernized industrial processes have been equipped with quite a few sensors to collect process-based data to find faults arising or prevailing in processes along with monitoring the status of processes. Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Due to the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional… More >